Senior Voice AI Engineer — HIPAA-Compliant AI Receptionist Platform

We are looking for a hands-on Voice AI Engineer to own the technical delivery of an enterprise voice automation program for an industrial equipment and services business.

You will take the solution from discovery and architecture through agent development, integrations, testing, deployment and ongoing optimisation. The initial focus is inbound calls across service, parts and rental, with approximately 300 calls per day.

This role suits someone who can turn a working demonstration into a reliable production service and work directly with customer teams to make it effective.

What you will build

A connected Voice AI solution that answers calls on existing business numbers, identifies customers and equipment, understands requests and routes conversations to the appropriate workflow.

The solution will capture structured call records, integrate with existing CRM and service-management systems, and transfer urgent or complex calls to people. Customer and equipment information will come from a database refreshed through scheduled data synchronisation.

Your responsibilities

• Own technical delivery: Translate business requirements into architecture, implementation plans, integration specifications and acceptance criteria.
• Build voice agents: Develop reception and specialist agents for service, parts and rental, with shared context and reliable hand-offs.
• Improve conversation quality: Configure speech recognition, voice generation and language models. Optimise latency, interruptions, turn-taking, pronunciation and handling of background noise.
• Implement telephony: Configure inbound routing, existing-number connectivity, concurrent calls, live transfers, after-hours handling and fallback routes.
• Connect business systems: Build APIs, webhooks and middleware for CRM, service queues and customer databases. Implement scheduled synchronisation, retries and duplicate prevention.
• Capture usable records: Generate accurate summaries and structured fields covering customer details, equipment, reason for calling, urgency, outcomes and relevant sales interest.
• Build safeguards: Implement call-duration and monthly-usage caps, alerts, escalation rules and checks that requests have been successfully logged.
• Test and launch: Develop realistic test scenarios, run integration and load testing, support customer acceptance testing and manage production deployment.
• Operate and improve: Monitor call quality, integration failures, latency and cost per minute. Investigate incidents and refine agent behaviour using transcripts and customer feedback.
• Document the solution: Maintain architecture diagrams, configuration records, deployment procedures and operational runbooks.

What we are looking for

• Strong development skills in Python and/or JavaScript/TypeScript.
• Experience shipping at least one Voice AI or conversational AI solution into production.
• Hands-on understanding of speech-to-text, text-to-speech, language models and tool/function calling.
• Experience with telephony APIs, SIP, call routing and human transfers.
• Strong API integration skills, including authentication, webhooks and asynchronous processing.
• Working knowledge of SQL databases, data synchronisation and cloud deployment.
• Experience with logging, monitoring, error handling and production troubleshooting.
• Ability to implement secure access, protect personal information and manage recordings and transcripts appropriately.
• Confidence working directly with customers, explaining technical trade-offs and taking ownership of delivery.

Useful additional experience

• Multi-agent orchestration and context management.
• Integration with enterprise CRM, workflow or document-management platforms.
• Retrieval-based knowledge systems and structured information extraction.
• Contact-centre, field-service, equipment rental or industrial service workflows.
• Automated conversation testing and evaluation.

What success looks like

• A stable deployment that meets agreed call-handling and integration targets.
• Clear, natural conversations with appropriate escalation to people.
• Accurate service requests and sales leads reaching the correct systems.
• Reliable controls over usage, cost and failure handling.
• Measurable improvements following testing and customer feedback.

Why join

You will own a live enterprise implementation from the first technical discussion through production operations. You will have direct access to decision-makers and the opportunity to build reusable components for future Voice AI deployments.

How to apply

Share your CV and a brief description of a Voice AI solution you have built. Explain your personal contribution, the integrations involved and one production challenge you solved.

The Role

We’re looking for a Senior Applied AI & Data Engineer to become our first dedicated AI and data engineer.

You’ll build conversational AI experiences across web, mobile, and in-store channels while developing the data foundation behind them. You’ll make key technical decisions and own your work through to production.

What You’ll Do

• Build AI assistants using tool calling to work with real product, search, and order systems

• Design guardrails and evaluation sets to ensure AI responses are accurate and safe

• Build real-time and voice-enabled AI experiences

• Improve product data quality through AI-assisted enrichment and review workflows

• Build data pipelines, analytics, and personalisation systems

• Work closely with web and mobile developers and help guide technical implementation

What You’ll Need

• 6+ years of experience building and running production backend systems

• Strong Python skills, plus experience with JavaScript/TypeScript backends

• Experience shipping at least one LLM-powered feature to real users

• Experience with search and relevance

• Experience building data pipelines and analytics stores

• Comfortable deploying and monitoring services on a major cloud platform

• Strong communication skills and the ability to work independently

Nice to Have

• Experience with speech or voice AI

• E-commerce or retail technology experience

• Experience building multilingual products

About the Company

The client is revolutionising the way businesses operate through cutting-edge technological solutions. Their focus is on developing intelligent agents and agentic workflows that automate processes and eliminate the need for human effort wherever possible. By leveraging

advanced AI and machine learning, they create systems that enhance productivity and drive efficiency.

Their expertise extends to the fintech, healthcare and medical technology sectors, where they develop innovative solutions that improve patient outcomes and streamline medical operations.

From medical devices to healthcare platforms, their work sits at the intersection of technology and medicine, pushing the boundaries of what's possible. The team is dedicated to continuous learning and growth, ensuring the team members are always at the forefront of the tech landscape.

About the Role

This is a senior, hands-on engineering role at the heart of our product team. You will be one of the most technical people in the room — setting the architecture for our real-time voice AI

agents and building the hardest parts of it yourself. From the systems that power live conversations to the interfaces our clients rely on, you will own how the product is engineered end to end.

We are looking for a genuine lead full-stack engineer with the depth to make architecture decisions that hold up as we scale, and the appetite to still be in the code every day. You should be as comfortable designing the backend services behind a live voice agent as you are shaping a clean interface on top of them — and comfortable being the person others turn to when something is hard.

You will work directly with the founder and product leadership on a fast-moving product, with real influence over technical direction. This is a role for someone who wants ownership at the level of "how the whole thing is built," not just individual features — and who raises the bar for

everyone around them.

What You'll Own

Set the technical direction

• Own the architecture of our core systems — the real-time voice agents, backend
• services, data and APIs — making the decisions that keep the product fast, reliable and scalable as it grows.
• Lead the hardest engineering problems and solve them personally.
• Establish engineering standards — code quality, review practices, testing and technical patterns that the team builds to.
• Drive technical strategy with the founder and product leadership — shaping the roadmap, flagging risk early, and turning product ambition into a sound technical plan.

Build the product end to end

• Design, build and ship features across the stack — backend services, APIs and front-ends — owning them from idea to production.
• Build the client-facing surfaces — dashboards, review tools and configuration interfaces that let our clients run and trust the product.
• Design and evolve the data models and APIs that hold up as we scale across clients.

Make it reliable and fast

• Own production quality — put the monitoring and alerting in place so issues are caught before clients feel them, and performance stays within target.
• Care about performance — find and fix bottlenecks across the stack.
• Build for correctness — put the testing and evaluation in place that keeps the product behaving predictably as it changes.

Lead through the team

• Mentor and grow engineers — through code review, pairing, and setting a technical example others learn from.
• Multiply the team's output — unblock others and lift the overall quality of the codebase.
• Take features from ambiguity to done — turn a rough product goal into a shipped, working capability with minimal hand-holding, and help others do the same.

What We're Looking For

• 8+ years of professional software engineering experience, with significant depth across backend and a track record of owning systems, not just features.
• Strong backend engineering, ideally in Python — building and scaling production services and APIs..
• Proven architecture and system-design ability — you have designed systems that scaled, and can reason clearly about trade-offs.
• Solid fundamentals across APIs, databases and cloud infrastructure.
• Experience building real-time and/or AI-powered products — or clear, demonstrable ability to lead in this area.
• A history of technical leadership — setting standards, mentoring engineers, and being trusted with the hardest problems — while remaining hands-on.
• Excellent communication and a genuine ownership mindset — someone who can be handed an ambiguous, high-stakes problem and be trusted to see it through.

Nice to Have

• Experience working with AI / large language models in production.
• Experience with voice or other real-time products.
• Exposure to healthcare, fintech, or other regulated / high-stakes domains.
• Experience as an early or senior engineer in a startup, where you set direction and wore many hats.

About Us

Invorto is our Voice AI product, bringing intelligent voice agents to real-world customer and operational use cases. Our voice pipeline is built in Python, running an STT → LLM → TTS architecture on top of the Pipecat framework.

This is a chance to work on hard problems in voice AI — latency, accuracy, naturalness, and reliability — building zero-to-one, owning your area end-to-end, and shipping to production at scale.

Note: This is a customer-facing role, and strong communication skills are essential.

About the Role

We're looking for a Voice AI Research Engineer to join the Invorto team and help build and continuously improve the voice AI systems that power our intelligent voice agents. This role is focused on the specialized craft of voice AI — designing evaluation and automation frameworks that ensure our STT, LLM, and TTS pipeline performs reliably in real-world, production conditions.

What You'll Do

• Design and build automated testing and quality frameworks for our STT → LLM → TTS voice pipeline, built on Pipecat
• Evaluate and benchmark STT, LLM, and TTS/ASR components on accuracy, latency, naturalness, and robustness across accents, languages, and real-world audio conditions
• Work hands-on with STT, TTS, and ASR models — fine-tuning, evaluating, and improving them for production use cases
• Identify failure modes and edge cases across the pipeline (background noise, accents, interruptions, turn-taking, latency, pipeline-stage handoffs) and build systems to catch them before production
• Collaborate closely with engineering to integrate quality checks and automation into the voice agent development lifecycle within the Pipecat-based architecture
• Research and stay current with advances in voice AI, and bring in new techniques, models, and tools to improve pipeline performance
• Work directly with customers to understand real-world voice use cases and translate them into evaluation criteria and quality benchmarks
• Partner with product and engineering to define what "production-grade quality" means for voice agents and drive the team toward it

What We're Looking For

• 4–6 years of experience, with a specialization in voice AI systems and automated quality evaluation
• Hands-on experience with STT (Speech-to-Text), TTS (Text-to-Speech), and ASR (Automatic Speech Recognition) models
• Experience designing and building automated testing/evaluation frameworks for voice or speech systems
• Strong understanding of what drives voice AI quality — accuracy, latency, naturalness, and robustness to real-world variability
• Strong programming skills in Python; familiarity with Pipecat or similar voice pipeline/orchestration frameworks is a plus
• Understanding of STT → LLM → TTS pipeline architectures and the trade-offs involved at each stage
• Research mindset — comfortable exploring new models, techniques, and tools and translating them into practical improvements
• Excellent communication skills — this is a customer-facing role, and you'll regularly engage directly with customers to understand needs and validate quality expectations

AI / Backend Engineer — Remote (India / Overseas)

Company: Ink'd

Compensation: $1,500–$2,500 USD/month, depending on experience

Type: Independent contractor, long-term opportunity

Ink'd is building a voice-first AI platform for residential real estate transactions. Our AI assistant allows real estate agents to generate contracts, automate workflows, and complete transaction documentation through natural conversation.

We're looking for a highly capable AI/backend engineer to help scale and maintain our production platform.

Responsibilities

• Build and maintain production AI agent workflows
• Develop integrations with third-party APIs and services
• Work with LLMs, structured data extraction, and document generation
• Troubleshoot production systems and improve reliability
• Build scalable backend services and maintain databases
• Collaborate directly with the founder and engineering team

Technical experience

• Python, FastAPI, Node.js, and REST APIs
• LLM APIs, tool calling, and agent orchestration
• PostgreSQL and cloud infrastructure
• GitHub, CI/CD, debugging, and deployment
• Experience with voice AI, WebSockets, or document processing is a major plus

What we're looking for

Someone who has independently shipped production software, can troubleshoot unfamiliar systems, communicates clearly, and takes ownership of their work.

Please include your GitHub, two examples of production systems you've built, your specific contributions, monthly compensation expectations, and availability.

We’re building a powerful, AI-driven communication platform — a next-generation alternative to RingCentral or 8x8 — powered by OpenAI, LangChain, and SIP/WebRTC. We're looking for a Full-Stack Software Developer who’s passionate about building real-time, AI-enabled voice infrastructure and who’s excited to work in a fast-moving, founder-led environment.

This is an opportunity to build from scratch, take ownership of core systems, and innovate on the edge of VoIP + AI.

What You’ll Do

• Design and build AI-driven voice and messaging features (e.g. smart IVRs, call transcription, virtual agents)
• Develop backend services using Python, Node.js, or Golang
• Integrate OpenAI, Whisper, and LangChain with real-time VoIP systems like Twilio, SIP, or WebRTC
• Create scalable APIs, handle call logic, and build AI pipelines
• Collaborate with the founder and early team on product strategy and infrastructure
• Participate in occasional in-person strategy meetings (Delhi, Bangalore, or nearby)

Must-Have Skills

• Strong programming experience in Python, Node.js, or Go
• Hands-on experience with VoIP/SIP, WebRTC, or tools like Twilio, Asterisk, Plivo
• Experience integrating with LLM APIs, OpenAI, or speech-to-text models
• Solid understanding of backend design, Docker, Redis, PostgreSQL
• Ability to work independently and deliver production-grade code

Nice to Have

• Familiarity with LangChain or agent-based AI systems
• Knowledge of call routing logic, STUN/TURN, or media servers (e.g. FreeSWITCH)
• Interest in building scalable cloud-first SaaS products

Work Setup

• 🏠 Remote work
• 🕐 Full-time role
• 💼 Direct collaboration with founder (technical)
• 🧘‍♂️ Flexible hours, strong ownership culture

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

About LeadSquared

LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services, and real estate. Headquartered in Bengaluru with offices across the US, UK, UAE, and Southeast Asia, we empower sales teams to close faster, smarter, and at scale.

Our AI team is at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows — building intelligent agents, copilots, and automation systems that redefine how businesses operate.

Role Overview

We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.

This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.

Key Responsibilities

•

Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.

•

Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.

•

Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.

•

Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.

•

Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.

•

Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.

•

Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.

•

Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.

•

Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.

Required Qualifications

Experience

•

2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.

•

Proven experience shipping LLM-based products or agentic AI systems into production environments.

Technical Skills

•

Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.

•

Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).

•

Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.

•

Solid understanding of RAG architectures, embedding models, and semantic search.

•

Experience with vector databases and similarity search infrastructure.

•

Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).

Problem-Solving & Mindset

•

Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.

•

Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.

•

Ability to balance research exploration with engineering pragmatism to ship reliable systems.

Preferred Qualifications

•

Experience with multi-agent orchestration and agent memory systems (short-term and long-term).

•

Familiarity with fine-tuning or RLHF workflows for domain adaptation.

•

Background in NLP, information retrieval, or conversational AI.

•

Prior experience in B2B SaaS or CRM domain is a plus.

•

Contributions to open-source AI/ML projects or published research/blogs.

•

Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services

Procedure is hiring for WorkHero.

WorkHero is building the AI-powered back office for the skilled trades, starting with the $50B+ HVAC industry. Small contractors are great at their trade but lose 20+ hours a week to invoicing, permits, scheduling, and paperwork. WorkHero combines expert office managers with automation and AI tooling, enabling a small team to take real ownership of that back-office work

We’re hiring a senior engineer to own our real-time voice stack end to end—AI agents operating on live phone calls—and the data platform that turns those calls into insight: call → transcript → events → warehouse → dashboards. You’ll own meaningful systems end to end alongside a small, senior team with deep experience in AI, product, and the trades.

What you’ll build

• New product screens and flows (jobs, customers, invoices, scheduling) in React and React Native, especially AI chat UI (chat & tool result rendering, streaming responses, human review and feedback loops)
• AI workflows in production: tool-using agents, RAG/search, classification/extraction, and human-in-the-loop flows
• Automations: Contribute new features and improvements to our AI-powered business automation platform

In addition, you’ll own our first investments into a realtime voice stack and the call-data platform behind it. For example:

• Realtime voice agents on live phone calls: telephony/WebRTC integration, streaming speech-to-text and text-to-speech, turn-taking, interruption handling, and latency optimization
• Voice pipeline reliability: backpressure, failover, graceful degradation, and monitoring for live calls
• Call-data pipeline: transcripts, events, and structured extraction flowing from every call into the warehouse
• Analytics & dashboards: data modeling and conversation-intelligence features on top of call data
• Evals & monitoring for voice agents: quality metrics, drift detection, and cost/latency tracking
• Cloud infrastructure: scaling our platform with infrastructure as code, queues and orchestration, and CI/CD

Responsibilities

• Analyze requirements and propose innovative AI-native solutions to technical problems
• Write clean scalable code
• Own the voice and data stack end-to-end: design, build, test, deploy, and operate
• Optimize the performance, latency, and cost of our real-time AI systems
• Respond to critical system issues and ensure continuous system reliability
• Mentor team members and collaborate across teams, especially with product and subject matter experts
• Work to understand the needs of our users and think creatively about how to solve design challenges in your work
• This is a Remote role. We expect a minimum 4 hours overlap with the WorkHero team (11 AM - 3 PM ET).

Qualifications

• Senior-level backend experience (typically 5+ years) shipping production systems that you've owned
• Hands-on experience with realtime voice or streaming systems: telephony (SIP/Twilio), WebRTC, streaming STT/TTS, or frameworks like LiveKit or Pipecat — or comparable experience with demanding realtime/streaming infrastructure
• Data engineering fundamentals: event pipelines, data modeling, warehousing, and analytics on production data
• Strong proficiency in a typed backend language (TypeScript preferred; comparable experience welcome)
• Hands-on experience with LLM-powered features (usage, prompting, optimization, etc) and AI architectures
• The ability to work with infrastructure as code (terraform), cloud, and CI/CD systems at scale. We're a small team, so we own the whole stack!
• Excitement to leverage AI coding tools to their maximum benefit. We love Claude Code and Cursor and are constantly looking for better ways to leverage our time to build fast and build for scale.

Nice to have

• experience with voice-AI platforms (Vapi, Retell, Bland, Deepgram, LiveKit) or conversation-intelligence products (e.g. Gong-style analytics)
• experience scaling cloud infrastructure, especially AWS, and how to get the most out of key AWS services
• experience with workflow automation tools like n8n or Lindy
• experience with React for building internal dashboards
• experience with HVAC or back-office business workflows

WorkHero is committed to building a diverse team. We encourage candidates from all backgrounds to apply.

About the company

We are an AI diffusion company. We partner with midmarket companies in asset-heavy, regulated industries to co-build deeply industry-specific agentic and machine learning solutions, which then get diffused across the industry through revenue-share and equity-linked models.

Our Past

We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.

Team

Our team of 20+ employees consists of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH. Our team includes academicians, IBM Research Fellows, and former founders. This role focuses on healthcare and RCM, working alongside our AI/ML engineers, data scientists and domain specialists.

Work you’ll do

We are looking for an experienced AI engineer with hands-on experience in healthcare and revenue cycle management (RCM). You will build and deploy AI agents, automations and data workflows for healthcare operations, with a focus on the US market.

The work includes payer-portal automation for claim status and EOB retrieval, voice agents for payer follow-up, analysis of claim journeys and bottlenecks, and AI-assisted insights into practice performance. You will work directly with billing and operations teams, owning delivery from understanding the workflow through deployment, evaluation and ongoing improvement.

Responsibilities

• Own AI agent and automation workflows from requirements through design, development, deployment and monitoring.
• Build agents that work across payer portals, healthcare documents, practice-management systems, EHR-connected tools and voice channels.
• Turn fragmented healthcare data into validated, structured inputs for automation and analysis.
• Work directly with billing and operations teams to understand workflows, resolve exceptions and improve systems based on user feedback.
• Evaluate workflow accuracy and completion rates using representative test sets. Monitor failures and operating costs, and implement retries, exception queues and human escalation
• Implement approved privacy and security controls for sensitive healthcare data, including access restrictions, audit trails and safeguards against exposing patient information in logs or model inputs.
• Diagnose and improve failures across data ingestion, integrations, agent execution and user-facing workflows.
• Measure operational outcomes, such as time saved, reduced manual effort and faster turnaround, with the teams using the systems.

Who you are

You are a passionate and results-oriented engineer who is driven by the potential of AI/ML to revolutionize processes, enhance products, and ultimately improve user experiences. You thrive in dynamic environments and are comfortable navigating ambiguity. You possess a strong sense of ownership and are eager to take initiative, advocating for your technical decisions while remaining open to feedback and collaboration.

You are adept at working with real-world, often imperfect data, and have a proven ability to develop, refine, and deploy AI/ML models into production in a cost-effective and scalable manner. You are excited by the prospect of making US healthcare operations faster and more reliable, for the people who run them and the patients they serve.

Skills & Requirements

Must have

• At least 2 years of professional AI engineering experience, including deploying and supporting systems used in real operations. Internships and academic projects alone do not meet this requirement.
• Hands-on experience building AI or automation solutions for healthcare RCM workflows, such as claim status checks, accounts receivable follow-up, denial management, payment posting or payer interactions.
• Practical understanding of how billing teams work, how claims move through the revenue cycle, and how incomplete data and exceptions affect execution.
• Experience deploying and supporting an LLM-based application or AI agent in production, using capabilities such as retrieval-augmented generation (RAG), tool calling or multi-step workflows. Ability to explain your engineering contribution, how the system was evaluated, what failed and how you addressed it.
• Strong production Python and SQL skills, experience integrating APIs, and working knowledge of JavaScript/TypeScript for browser automation or application integration.
• Experience automating workflows across APIs, browsers or user interfaces, using tools such as Playwright, Selenium or UiPath, with error handling and human escalation where needed.
• Experience deploying and monitoring applications on at least one cloud platform—AWS, Azure or GCP—using containers, version control and automated testing.
• Experience handling imperfect inputs such as scanned PDFs, inconsistent portal layouts or incomplete records, with validation and exception handling.
• Experience working directly with billing or operations teams, improving systems based on their feedback, and demonstrating outcomes such as time saved, claims processed or reduced turnaround time.
• Experience handling sensitive healthcare data through appropriate access controls, secure logging and approved data-handling practices.

Preferred experience

• Experience delivering solutions for US healthcare providers, RCM companies or payer-facing operations, including working with US billing teams and payer workflows.
• Experience with practice-management or EHR integrations, payer portals, explanation of benefits (EOB) documents or electronic remittance advice (ERA) data.
• Experience analyzing claim histories, revenue-cycle bottlenecks or practice performance.
• Eperience building voice agents or integrating speech-to-text and text-to-speech services is a plus.
• Familiarity with Model Context Protocol (MCP) integrations.
• Experience with Kubernetes, serverless applications or event-driven architectures.

Working with us

We are a young company, but we believe strongly in thinking long-term, while acting with urgency. Our ethos is rooted in innovation, efficiency and high-quality outcomes. We believe the future of work is AI-augmented and boundaryless.

Here are some of our guiding principles:

• Think in decades. Act in hours. As an independent company, our moat is time. While our decisions are for the long-term horizon, our execution will be fast – measured in hours and days, not weeks and months.
• Own the canvas. Throw yourself in to build, fix or improve – anything that isn’t done right, irrespective of who did it. Be selfish about improving across the organization – because once the rot sets in, we waste years in surgery and recovery.
• Use data or don’t use data. Use data where you ought to but not as a ‘cover-my-back’ political tool. Be capable of making decisions with partial or limited data. Get better at intuition and pattern-matching. Whichever way you go, be mostly right about it.
• Avoid work about work. Process creeps on purpose, unless we constantly question it. We are deliberate about committing to rituals that take time away from the actual work. We truly believe that a meeting that could be an email, should be an email and you don’t need a person with the highest title to say that out loud.
• High revenue per person. We work backwards from this metric. Our default is to automate instead of hiring. We multi-skill our people to own more outcomes than hiring someone who has less to do. We don’t like squatting and hoarding that comes in the form of hiring for growth. High revenue per person comes from high quality work from everyone. We demand it.